Kim Han-Kyul, Kang Ji-One, Lim Ji Eun, Ha Tae-Woong, Jung Hae Un, Lee Won Jun, Kim Dong Jun, Baek Eun Ju, Adcock Ian M, Chung Kian Fan, Kim Tae-Bum, Oh Bermseok
Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea.
Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea.
Clin Transl Allergy. 2023 Jul;13(7):e12282. doi: 10.1002/clt2.12282.
The extent of differences between genetic risks associated with various asthma subtypes is still unknown. To better understand the heterogeneity of asthma, we employed an unsupervised method to identify genetic variants specifically associated with asthma subtypes. Our goal was to gain insight into the genetic basis of asthma.
In this study, we utilized the UK Biobank dataset to select asthma patients (All asthma, n = 50,517) and controls (n = 283,410). We excluded 14,431 individuals who had no information on predicted values of forced expiratory volume in one second percent (FEV1%) and onset age, resulting in a final total of 36,086 asthma cases. We conducted k-means clustering based on asthma onset age and predicted FEV1% using these samples (n = 36,086). Cluster-specific genome-wide association studies were then performed, and heritability was estimated via linkage disequilibrium score regression. To further investigate the pathophysiology, we conducted eQTL analysis with GTEx and gene-set enrichment analysis with FUMA.
Clustering resulted in four distinct clusters: early onset asthma (early onset with normal lung function, n = 8172), early onset asthma (early onset with reduced lung function, n = 8925), late-onset asthma (late-onset with normal lung function, n = 12,481), and late-onset asthma (late-onset with reduced lung function, n = 6508). Our GWASs in four clusters and in All asthma sample identified 5 novel loci, 14 novel signals, and 51 cluster-specific signals. Among clusters, early onset asthma and late-onset asthma were the least correlated (r = 0.37). Early onset asthma showed the highest heritability explained by common variants (h = 0.212) and was associated with the largest number of variants (71 single nucleotide polymorphisms). Further, the pathway analysis conducted through eQTL and gene-set enrichment analysis showed that the worsening of symptoms in early onset asthma correlated with lymphocyte activation, pathogen recognition, cytokine receptor activation, and lymphocyte differentiation.
Our findings suggest that early onset asthma was the most genetically predisposed cluster, and that asthma clusters with reduced lung function were genetically distinct from clusters with normal lung function. Our study revealed the genetic variation between clusters that were segmented based on onset age and lung function, providing an important clue for the genetic mechanism of asthma heterogeneity.
与各种哮喘亚型相关的遗传风险之间的差异程度尚不清楚。为了更好地理解哮喘的异质性，我们采用了一种无监督方法来识别与哮喘亚型特异性相关的基因变异。我们的目标是深入了解哮喘的遗传基础。
在本研究中，我们利用英国生物银行数据集选择哮喘患者（所有哮喘患者，n = 50,517）和对照组（n = 283,410）。我们排除了14,431名没有一秒用力呼气容积百分比（FEV1%）预测值和发病年龄信息的个体，最终共有36,086例哮喘病例。我们使用这些样本（n = 36,086）基于哮喘发病年龄和预测的FEV1%进行k均值聚类。然后进行特定聚类的全基因组关联研究，并通过连锁不平衡评分回归估计遗传力。为了进一步研究病理生理学，我们使用GTEx进行eQTL分析，并使用FUMA进行基因集富集分析。
聚类产生了四个不同的聚类：早发性哮喘（肺功能正常的早发性，n = 8172）、早发性哮喘（肺功能降低的早发性，n = 8925）、晚发性哮喘（肺功能正常的晚发性，n = 12,481）和晚发性哮喘（肺功能降低的晚发性，n = 6508）。我们在四个聚类和所有哮喘样本中的全基因组关联研究确定了5个新位点、14个新信号和51个聚类特异性信号。在各聚类中，早发性哮喘和晚发性哮喘的相关性最低（r = 0.37）。早发性哮喘显示出由常见变异解释的最高遗传力（h = 0.212），并且与最多数量的变异相关（71个单核苷酸多态性）。此外，通过eQTL和基因集富集分析进行的通路分析表明，早发性哮喘症状的恶化与淋巴细胞活化、病原体识别、细胞因子受体活化和淋巴细胞分化相关。
我们的研究结果表明，早发性哮喘是遗传易感性最高的聚类，并且肺功能降低的哮喘聚类在遗传上与肺功能正常的聚类不同。我们的研究揭示了基于发病年龄和肺功能划分的聚类之间的遗传变异，为哮喘异质性的遗传机制提供了重要线索。
